update app

gradio_imagecompletion.py

@@ -8,6 +8,7 @@ from PIL import Image
 import matplotlib.pyplot as plt
 
 import os
+import numpy as np
 import requests
 from glob import glob
 import gradio as gr
@@ -35,15 +36,46 @@ model.to(device)
 
 def process_image(image):
     logger.info('--- image file received')
-    return image.name
+    # prepare 7 images, shape (7, 1024)
+    batch_size = 7
+    encoding = feature_extractor([image for _ in range(batch_size)], return_tensors="pt")
+    # create primers
+    samples = encoding.pixel_values.numpy()
+    n_px = feature_extractor.size
+    clusters = feature_extractor.clusters
+    n_px_crop = 16
+    primers = samples.reshape(-1,n_px*n_px)[:,:n_px_crop*n_px] # crop top n_px_crop rows. These will be the conditioning tokens
+    
+    # get conditioned image (from first primer tensor), padded with black pixels to be 32x32
+    primers_img = np.reshape(np.rint(127.5 * (clusters[primers[0]] + 1.0)), [n_px_crop,n_px, 3]).astype(np.uint8) 
+    primers_img = np.pad(primers_img, pad_width=((0,16), (0,0), (0,0)), mode="constant")
+    
+    # generate (no beam search)
+    context = np.concatenate((np.full((batch_size, 1), model.config.vocab_size - 1), primers), axis=1)
+    context = torch.tensor(context).to(device)
+    output = model.generate(input_ids=context, max_length=n_px*n_px + 1, temperature=1.0, do_sample=True, top_k=40)
+    # decode back to images (convert color cluster tokens back to pixels)
+    samples = output[:,1:].cpu().detach().numpy()
+    samples_img = [np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [n_px, n_px, 3]).astype(np.uint8) for s in samples] 
+    
+    samples_img = [primers_img] + samples_img
+    
+    # stack images horizontally
+    row1 = np.hstack(samples_img[:4])
+    row2 = np.hstack(samples_img[4:])
+    result = np.vstack([row1, row2])
+    
+    # return as PIL Image
+    completion = Image.fromarray(result)
+    return completion
 
 
 iface = gr.Interface(
     process_image,
     title="이미지의 절반을 지우고 절반을 채워 넣어주는 Image Completion 데모입니다 (ImageGPT)",
     description='주어진 이미지의 절반 아래를 AI가 채워 넣어줍니다',
-    inputs=gr.inputs.Image(type="pil"),
-    outputs=gr.outputs.Image(type="pil", label="Model input + completions"),
+    inputs=gr.inputs.Image(type="pil", label='인풋 이미지'),
+    outputs=gr.outputs.Image(type="pil", label='AI가 그린 결과'),
     examples=examples,
     enable_queue=True,
     article='<p style="text-align:center">i-Scream AI</p>',

requirements.txt

@@ -4,3 +4,4 @@ torch==1.9.0
 loguru==0.5.3
 transformers==4.13.0
 Pillow==8.4.0
+numppy==1.19.5